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Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.
Manchanda, Chirag; Harley, Robert A; Marshall, Julian D; Turner, Alexander J; Apte, Joshua S.
Afiliación
  • Manchanda C; Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States.
  • Harley RA; Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States.
  • Marshall JD; Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States.
  • Turner AJ; Department of Atmospheric Sciences, University of Washington, Seattle, Washington 98195, United States.
  • Apte JS; Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States.
Environ Sci Technol ; 58(28): 12563-12574, 2024 Jul 16.
Article en En | MEDLINE | ID: mdl-38950186
ABSTRACT
Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Contaminantes Atmosféricos / Contaminación del Aire Idioma: En Revista: Environ Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Contaminantes Atmosféricos / Contaminación del Aire Idioma: En Revista: Environ Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos